{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sweeps - Eigenmode matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prerequisite\n",
    "You need to have a working local installation of Ansys"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Perform the necessary imports and create a QDesign in Metal first."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import qiskit_metal as metal\n",
    "from qiskit_metal import designs, draw\n",
    "from qiskit_metal import MetalGUI, Dict, Headings\n",
    "from qiskit_metal.analyses.quantization import EPRanalysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Create the design in Metal\n",
    "# Create a design by specifying the chip size and open Metal GUI.\n",
    "\n",
    "design = designs.DesignPlanar({}, True)\n",
    "design.chips.main.size['size_x'] = '2mm'\n",
    "design.chips.main.size['size_y'] = '2mm'\n",
    "\n",
    "gui = MetalGUI(design)\n",
    "\n",
    "from qiskit_metal.qlibrary.qubits.transmon_pocket import TransmonPocket\n",
    "from qiskit_metal.qlibrary.terminations.open_to_ground import OpenToGround\n",
    "from qiskit_metal.qlibrary.tlines.meandered import RouteMeander"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### In this example, the design consists of 1 qubit and 1 CPW connected to OpenToGround."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Allow running the same cell here multiple times to overwrite changes\n",
    "design.overwrite_enabled = True\n",
    "\n",
    "# Remove all qcomponents from GUI.\n",
    "design.delete_all_components()\n",
    "\n",
    "# So as to demonstrate the quality factor outputs easily, the\n",
    "#subtrate material type is being changed to FR4_epoxy from the\n",
    "#default of silicon\n",
    "design.chips.main.material = 'FR4_epoxy'\n",
    "\n",
    "q1 = TransmonPocket(\n",
    "    design,\n",
    "    'Q1',\n",
    "    options=dict(pad_width='425 um',\n",
    "                 pocket_height='650um', \n",
    "                 hfss_inductance = '17nH',\n",
    "                 connection_pads=dict(\n",
    "                     readout=dict(loc_W=+1, loc_H=+1, pad_width='200um'))))\n",
    "otg = OpenToGround(design,\n",
    "                   'open_to_ground',\n",
    "                   options=dict(pos_x='1.75mm', pos_y='0um', orientation='0'))\n",
    "readout = RouteMeander(\n",
    "    design, 'readout',\n",
    "    Dict(\n",
    "        total_length='6 mm',\n",
    "        hfss_wire_bonds = True,\n",
    "        fillet='90 um',\n",
    "        lead=dict(start_straight='100um'),\n",
    "        pin_inputs=Dict(start_pin=Dict(component='Q1', pin='readout'),\n",
    "                        end_pin=Dict(component='open_to_ground', pin='open')),\n",
    "    ))\n",
    "\n",
    "gui.rebuild()\n",
    "gui.autoscale()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": [
     "nbsphinx-thumbnail"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "image/png": {
       "width": 500
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gui.screenshot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2 Metal passes information to 'hfss' simulator, and gets a solution matrix.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Create a separate analysis object for the combined qbit+readout.\n",
    "eig_qres = EPRanalysis(design, \"hfss\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Prepare data to pass as arguments for method run_sweep().  \n",
    "\n",
    "Method run_sweep() will open the simulation software if software is not open already."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "### for render_design()\n",
    "# Render every QComponent in QDesign.\n",
    "render_qcomps = []\n",
    "\n",
    "# Identify which kind of pins in Ansys. \n",
    "# Follow details from renderer in\n",
    "# QHFSSRenderer.render_design.\n",
    "# No pins are open, so don't need to utilize render_endcaps.\n",
    "open_terminations = []\n",
    "\n",
    "#List of tuples of jj's that shouldn't be rendered.  \n",
    "#Follow details from renderer in QHFSSRenderer.render_design.\n",
    "render_ignored_jjs = []\n",
    "\n",
    "# Either calculate a bounding box based on the location of \n",
    "# rendered geometries or use chip size from design class.\n",
    "box_plus_buffer = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# For simulator hfss, the setup options are :\n",
    "# min_freq_ghz, n_modes, max_delta_f, max_passes, min_passes, min_converged=None,\n",
    "# pct_refinement, basis_order\n",
    "\n",
    "# If you don't pass all the arguments, the default is determined by \n",
    "# QHFSSRenderer's default_options.\n",
    "\n",
    "# If a setup named \"sweeper_em_setup\" exists in the project, it will be deleted, \n",
    "# and a new setup will be added.\n",
    "\n",
    "eig_qres.sim.setup.name=\"sweeper_em_setup\"\n",
    "eig_qres.sim.setup.min_freq_ghz=4\n",
    "eig_qres.sim.setup.n_modes=2\n",
    "eig_qres.sim.setup.max_passes=15\n",
    "eig_qres.sim.setup.min_converged = 2\n",
    "eig_qres.sim.setup.max_delta_f = 0.2\n",
    "\n",
    "eig_qres.setup.junctions.jj.rect = 'JJ_rect_Lj_Q1_rect_jj'\n",
    "eig_qres.setup.junctions.jj.line = 'JJ_Lj_Q1_rect_jj_'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "- Connect to Ansys HFSS, eigenmode solution.\n",
    "- Rebuild QComponents in Metal.\n",
    "- Render QComponents within HFSS and setup.\n",
    "- Delete/Clear the HFSS between each calculation of solution matrix.\n",
    "- Calculate solution matrix for each value in option_sweep.\n",
    "\n",
    "#### Return a dict and return code.  If the return code is zero, there were no errors detected.  \n",
    "#### The dict has:  key = each value used to sweep, value = data from simulators\n",
    "\n",
    "#### This could take minutes based size of design."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO 08:11AM [connect_project]: Connecting to Ansys Desktop API...\n",
      "INFO 08:11AM [load_ansys_project]: \tOpened Ansys App\n",
      "INFO 08:11AM [load_ansys_project]: \tOpened Ansys Desktop v2020.2.0\n",
      "INFO 08:11AM [load_ansys_project]: \tOpened Ansys Project\n",
      "\tFolder:    C:/Ansoft/\n",
      "\tProject:   Project23\n",
      "INFO 08:11AM [connect_design]: No active design found (or error getting active design).\n",
      "INFO 08:11AM [connect]: \t Connected to project \"Project23\". No design detected\n",
      "INFO 08:11AM [connect_design]: \tOpened active design\n",
      "\tDesign:    GetEigenModeSolution_hfss [Solution type: Eigenmode]\n",
      "WARNING 08:11AM [connect_setup]: \tNo design setup detected.\n",
      "WARNING 08:11AM [connect_setup]: \tCreating eigenmode default setup.\n",
      "INFO 08:11AM [get_setup]: \tOpened setup `Setup`  (<class 'pyEPR.ansys.HfssEMSetup'>)\n",
      "INFO 08:11AM [get_setup]: \tOpened setup `sweeper_em_setup`  (<class 'pyEPR.ansys.HfssEMSetup'>)\n",
      "INFO 08:11AM [analyze]: Analyzing setup sweeper_em_setup\n",
      "08:26AM 34s INFO [get_f_convergence]: Saved convergences to C:\\workspace\\qiskit-metal\\docs\\tut\\4-Analysis\\hfss_eig_f_convergence.csv\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Design \"GetEigenModeSolution_hfss\" info:\n",
      "\t# eigenmodes    2\n",
      "\t# variations    1\n",
      "Design \"GetEigenModeSolution_hfss\" info:\n",
      "\t# eigenmodes    2\n",
      "\t# variations    1\n",
      "\n",
      "        energy_elec_all       = 5.19788159957566e-25\n",
      "        energy_elec_substrate = 4.2287113157029e-25\n",
      "        EPR of substrate = 81.4%\n",
      "\n",
      "        energy_mag    = 8.01738203686217e-27\n",
      "        energy_mag % of energy_elec_all  = 1.5%\n",
      "        \n",
      "\n",
      "Variation 0  [1/1]\n",
      "\n",
      "  \u001b[1mMode 0 at 7.53 GHz   [1/2]\u001b[0m\n",
      "    Calculating ℰ_magnetic,ℰ_electric\n",
      "       (ℰ_E-ℰ_H)/ℰ_E       ℰ_E       ℰ_H\n",
      "               98.5%  2.599e-25 4.009e-27\n",
      "\n",
      "    Calculating junction energy participation ration (EPR)\n",
      "\tmethod=`line_voltage`. First estimates:\n",
      "\tjunction        EPR p_0j   sign s_0j    (p_capacitive)\n",
      "\t\tEnergy fraction (Lj over Lj&Cj)= 95.71%\n",
      "\tjj                1.6736  (+)        0.0750176\n",
      "\t\t(U_tot_cap-U_tot_ind)/mean=-22.21%\n",
      "WARNING: This simulation must not have converged well!!!                The difference in the total cap and ind energies is larger than 10%.                Proceed with caution.\n",
      "Calculating Qdielectric_main for mode 0 (0/1)\n",
      "p_dielectric_main_0 = 0.8135451403987578\n",
      "\n",
      "  \u001b[1mMode 1 at 8.86 GHz   [2/2]\u001b[0m\n",
      "    Calculating ℰ_magnetic,ℰ_electric\n",
      "       (ℰ_E-ℰ_H)/ℰ_E       ℰ_E       ℰ_H\n",
      "                1.2%  5.877e-25 5.809e-25\n",
      "\n",
      "    Calculating junction energy participation ration (EPR)\n",
      "\tmethod=`line_voltage`. First estimates:\n",
      "\tjunction        EPR p_1j   sign s_1j    (p_capacitive)\n",
      "\t\tEnergy fraction (Lj over Lj&Cj)= 94.17%\n",
      "\tjj              0.0199851  (+)        0.00123759\n",
      "\t\t(U_tot_cap-U_tot_ind)/mean=-0.36%\n",
      "Calculating Qdielectric_main for mode 1 (1/1)\n",
      "p_dielectric_main_1 = 0.8119553568702682\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING 08:27AM [__init__]: <p>Error: <class 'IndexError'></p>\n",
      "ERROR 08:27AM [_get_participation_normalized]: WARNING: U_tot_cap-U_tot_ind / mean = 44.4% is > 15%.                     \n",
      "Is the simulation converged? Proceed with caution\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "ANALYSIS DONE. Data saved to:\n",
      "\n",
      "C:\\data-pyEPR\\Project23\\GetEigenModeSolution_hfss\\2021-08-18 08-26-35.npz\n",
      "\n",
      "\n",
      "\t Differences in variations:\n",
      "\n",
      "\n",
      "\n",
      " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . \n",
      "Variation 0\n",
      "\n",
      "Starting the diagonalization\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ERROR 08:27AM [_get_participation_normalized]: WARNING: U_tot_cap-U_tot_ind / mean = 44.4% is > 15%.                     \n",
      "Is the simulation converged? Proceed with caution\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished the diagonalization\n",
      "Pm_norm=\n",
      "modes\n",
      "0    0.635168\n",
      "1    0.817290\n",
      "dtype: float64\n",
      "\n",
      "Pm_norm idx =\n",
      "      jj\n",
      "0   True\n",
      "1  False\n",
      "*** P (participation matrix, not normlz.)\n",
      "         jj\n",
      "0  1.556815\n",
      "1  0.019960\n",
      "\n",
      "*** S (sign-bit matrix)\n",
      "   s_jj\n",
      "0     1\n",
      "1     1\n",
      "*** P (participation matrix, normalized.)\n",
      "      0.99\n",
      "      0.02\n",
      "\n",
      "*** Chi matrix O1 PT (MHz)\n",
      "    Diag is anharmonicity, off diag is full cross-Kerr.\n",
      "       424     20.1\n",
      "      20.1    0.239\n",
      "\n",
      "*** Chi matrix ND (MHz) \n",
      "       499     10.1\n",
      "      10.1   0.0723\n",
      "\n",
      "*** Frequencies O1 PT (MHz)\n",
      "0    7100.043062\n",
      "1    8845.729528\n",
      "dtype: float64\n",
      "\n",
      "*** Frequencies ND (MHz)\n",
      "0    7067.159806\n",
      "1    8847.889665\n",
      "dtype: float64\n",
      "\n",
      "*** Q_coupling\n",
      "Empty DataFrame\n",
      "Columns: []\n",
      "Index: [0, 1]\n"
     ]
    },
    {
     "data": {
      "text/markdown": [
       "#### Mode frequencies (MHz)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "###### Numerical diagonalization"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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       "      <th>Lj</th>\n",
       "      <th>10</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eigenmode</th>\n",
       "      <th></th>\n",
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       "Lj              10\n",
       "eigenmode         \n",
       "0          7100.04\n",
       "1          8845.73"
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    {
     "data": {
      "text/markdown": [
       "#### Kerr Non-linear coefficient table (MHz)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
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     "data": {
      "text/markdown": [
       "###### Numerical diagonalization"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
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       "      <th rowspan=\"2\" valign=\"top\">10</th>\n",
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     "text": [
      "INFO 08:27AM [connect_design]: \tOpened active design\n",
      "\tDesign:    GetEigenModeSolution_hfss [Solution type: Eigenmode]\n",
      "INFO 08:27AM [get_setup]: \tOpened setup `sweeper_em_setup`  (<class 'pyEPR.ansys.HfssEMSetup'>)\n",
      "INFO 08:27AM [analyze]: Analyzing setup sweeper_em_setup\n",
      "08:36AM 21s INFO [get_f_convergence]: Saved convergences to C:\\workspace\\qiskit-metal\\docs\\tut\\4-Analysis\\hfss_eig_f_convergence.csv\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Design \"GetEigenModeSolution_hfss\" info:\n",
      "\t# eigenmodes    2\n",
      "\t# variations    1\n",
      "Design \"GetEigenModeSolution_hfss\" info:\n",
      "\t# eigenmodes    2\n",
      "\t# variations    1\n",
      "\n",
      "        energy_elec_all       = 1.24548064887814e-24\n",
      "        energy_elec_substrate = 1.01220397065148e-24\n",
      "        EPR of substrate = 81.3%\n",
      "\n",
      "        energy_mag    = 6.56191412175113e-26\n",
      "        energy_mag % of energy_elec_all  = 5.3%\n",
      "        \n",
      "\n",
      "Variation 0  [1/1]\n",
      "\n",
      "  \u001b[1mMode 0 at 7.47 GHz   [1/2]\u001b[0m\n",
      "    Calculating ℰ_magnetic,ℰ_electric\n",
      "       (ℰ_E-ℰ_H)/ℰ_E       ℰ_E       ℰ_H\n",
      "               94.7%  6.227e-25 3.281e-26\n",
      "\n",
      "    Calculating junction energy participation ration (EPR)\n",
      "\tmethod=`line_voltage`. First estimates:\n",
      "\tjunction        EPR p_0j   sign s_0j    (p_capacitive)\n",
      "\t\tEnergy fraction (Lj over Lj&Cj)= 95.78%\n",
      "\tjj               1.61044  (+)        0.0709035\n",
      "\t\t(U_tot_cap-U_tot_ind)/mean=-21.66%\n",
      "WARNING: This simulation must not have converged well!!!                The difference in the total cap and ind energies is larger than 10%.                Proceed with caution.\n",
      "Calculating Qdielectric_main for mode 0 (0/1)\n",
      "p_dielectric_main_0 = 0.8127014832090866\n",
      "\n",
      "  \u001b[1mMode 1 at 8.07 GHz   [2/2]\u001b[0m\n",
      "    Calculating ℰ_magnetic,ℰ_electric\n",
      "       (ℰ_E-ℰ_H)/ℰ_E       ℰ_E       ℰ_H\n",
      "                4.9%  7.606e-25 7.235e-25\n",
      "\n",
      "    Calculating junction energy participation ration (EPR)\n",
      "\tmethod=`line_voltage`. First estimates:\n",
      "\tjunction        EPR p_1j   sign s_1j    (p_capacitive)\n",
      "\t\tEnergy fraction (Lj over Lj&Cj)= 95.11%\n",
      "\tjj              0.0831536  (+)        0.00427873\n",
      "\t\t(U_tot_cap-U_tot_ind)/mean=-1.48%\n",
      "Calculating Qdielectric_main for mode 1 (1/1)\n",
      "p_dielectric_main_1 = 0.8110869059274507\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING 08:36AM [__init__]: <p>Error: <class 'IndexError'></p>\n",
      "ERROR 08:36AM [_get_participation_normalized]: WARNING: U_tot_cap-U_tot_ind / mean = 43.3% is > 15%.                     \n",
      "Is the simulation converged? Proceed with caution\n",
      "ERROR 08:36AM [_get_participation_normalized]: WARNING: U_tot_cap-U_tot_ind / mean = 43.3% is > 15%.                     \n",
      "Is the simulation converged? Proceed with caution\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "ANALYSIS DONE. Data saved to:\n",
      "\n",
      "C:\\data-pyEPR\\Project23\\GetEigenModeSolution_hfss\\2021-08-18 08-36-22.npz\n",
      "\n",
      "\n",
      "\t Differences in variations:\n",
      "\n",
      "\n",
      "\n",
      " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . \n",
      "Variation 0\n",
      "\n",
      "Starting the diagonalization\n",
      "Finished the diagonalization\n",
      "Pm_norm=\n",
      "modes\n",
      "0    0.639346\n",
      "1    0.806611\n",
      "dtype: float64\n",
      "\n",
      "Pm_norm idx =\n",
      "      jj\n",
      "0   True\n",
      "1  False\n",
      "*** P (participation matrix, not normlz.)\n",
      "         jj\n",
      "0  1.503817\n",
      "1  0.082799\n",
      "\n",
      "*** S (sign-bit matrix)\n",
      "   s_jj\n",
      "0     1\n",
      "1     1\n",
      "*** P (participation matrix, normalized.)\n",
      "      0.96\n",
      "     0.083\n",
      "\n",
      "*** Chi matrix O1 PT (MHz)\n",
      "    Diag is anharmonicity, off diag is full cross-Kerr.\n",
      "       394     73.4\n",
      "      73.4     3.42\n",
      "\n",
      "*** Chi matrix ND (MHz) \n",
      "       532     18.8\n",
      "      18.8    0.297\n",
      "\n",
      "*** Frequencies O1 PT (MHz)\n",
      "0    7036.474625\n",
      "1    8032.650030\n",
      "dtype: float64\n",
      "\n",
      "*** Frequencies ND (MHz)\n",
      "0    6983.674726\n",
      "1    8048.708620\n",
      "dtype: float64\n",
      "\n",
      "*** Q_coupling\n",
      "Empty DataFrame\n",
      "Columns: []\n",
      "Index: [0, 1]\n"
     ]
    },
    {
     "data": {
      "text/markdown": [
       "#### Mode frequencies (MHz)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
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     "data": {
      "text/markdown": [
       "###### Numerical diagonalization"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
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       "      <th>Lj</th>\n",
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       "    <tr>\n",
       "      <th>eigenmode</th>\n",
       "      <th></th>\n",
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       "eigenmode         \n",
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     "data": {
      "text/markdown": [
       "#### Kerr Non-linear coefficient table (MHz)"
      ],
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       "<IPython.core.display.Markdown object>"
      ]
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     "data": {
      "text/markdown": [
       "###### Numerical diagonalization"
      ],
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18.82</td>\n",
       "      <td>0.30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           0      1\n",
       "Lj                 \n",
       "10 0  532.36  18.82\n",
       "   1   18.82   0.30"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO 08:36AM [connect_design]: \tOpened active design\n",
      "\tDesign:    GetEigenModeSolution_hfss [Solution type: Eigenmode]\n",
      "INFO 08:37AM [get_setup]: \tOpened setup `sweeper_em_setup`  (<class 'pyEPR.ansys.HfssEMSetup'>)\n",
      "INFO 08:37AM [analyze]: Analyzing setup sweeper_em_setup\n",
      "08:49AM 07s INFO [get_f_convergence]: Saved convergences to C:\\workspace\\qiskit-metal\\docs\\tut\\4-Analysis\\hfss_eig_f_convergence.csv\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Design \"GetEigenModeSolution_hfss\" info:\n",
      "\t# eigenmodes    2\n",
      "\t# variations    1\n",
      "Design \"GetEigenModeSolution_hfss\" info:\n",
      "\t# eigenmodes    2\n",
      "\t# variations    1\n",
      "\n",
      "        energy_elec_all       = 1.24148729817218e-24\n",
      "        energy_elec_substrate = 1.00761523148401e-24\n",
      "        EPR of substrate = 81.2%\n",
      "\n",
      "        energy_mag    = 9.86581588344368e-25\n",
      "        energy_mag % of energy_elec_all  = 79.5%\n",
      "        \n",
      "\n",
      "Variation 0  [1/1]\n",
      "\n",
      "  \u001b[1mMode 0 at 7.33 GHz   [1/2]\u001b[0m\n",
      "    Calculating ℰ_magnetic,ℰ_electric\n",
      "       (ℰ_E-ℰ_H)/ℰ_E       ℰ_E       ℰ_H\n",
      "               20.5%  6.207e-25 4.933e-25\n",
      "\n",
      "    Calculating junction energy participation ration (EPR)\n",
      "\tmethod=`line_voltage`. First estimates:\n",
      "\tjunction        EPR p_0j   sign s_0j    (p_capacitive)\n",
      "\t\tEnergy fraction (Lj over Lj&Cj)= 95.93%\n",
      "\tjj              0.349277  (+)        0.0148271\n",
      "\t\t(U_tot_cap-U_tot_ind)/mean=-5.98%\n",
      "Calculating Qdielectric_main for mode 0 (0/1)\n",
      "p_dielectric_main_0 = 0.8116194446511892\n",
      "\n",
      "  \u001b[1mMode 1 at 7.62 GHz   [2/2]\u001b[0m\n",
      "    Calculating ℰ_magnetic,ℰ_electric\n",
      "       (ℰ_E-ℰ_H)/ℰ_E       ℰ_E       ℰ_H\n",
      "               79.1%  2.755e-25 5.769e-26\n",
      "\n",
      "    Calculating junction energy participation ration (EPR)\n",
      "\tmethod=`line_voltage`. First estimates:\n",
      "\tjunction        EPR p_1j   sign s_1j    (p_capacitive)\n",
      "\t\tEnergy fraction (Lj over Lj&Cj)= 95.61%\n",
      "\tjj                1.3439  (+)        0.0616636\n",
      "\t\t(U_tot_cap-U_tot_ind)/mean=-18.80%\n",
      "WARNING: This simulation must not have converged well!!!                The difference in the total cap and ind energies is larger than 10%.                Proceed with caution.\n",
      "Calculating Qdielectric_main for mode 1 (1/1)\n",
      "p_dielectric_main_1 = 0.8133632043452139\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING 08:49AM [__init__]: <p>Error: <class 'IndexError'></p>\n",
      "ERROR 08:49AM [_get_participation_normalized]: WARNING: U_tot_cap-U_tot_ind / mean = 37.6% is > 15%.                     \n",
      "Is the simulation converged? Proceed with caution\n",
      "ERROR 08:49AM [_get_participation_normalized]: WARNING: U_tot_cap-U_tot_ind / mean = 37.6% is > 15%.                     \n",
      "Is the simulation converged? Proceed with caution\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "ANALYSIS DONE. Data saved to:\n",
      "\n",
      "C:\\data-pyEPR\\Project23\\GetEigenModeSolution_hfss\\2021-08-18 08-49-08.npz\n",
      "\n",
      "\n",
      "\t Differences in variations:\n",
      "\n",
      "\n",
      "\n",
      " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . \n",
      "Variation 0\n",
      "\n",
      "Starting the diagonalization\n",
      "Finished the diagonalization\n",
      "Pm_norm=\n",
      "modes\n",
      "0    0.766393\n",
      "1    0.663454\n",
      "dtype: float64\n",
      "\n",
      "Pm_norm idx =\n",
      "     jj\n",
      "0  True\n",
      "1  True\n",
      "*** P (participation matrix, not normlz.)\n",
      "         jj\n",
      "0  0.344174\n",
      "1  1.265842\n",
      "\n",
      "*** S (sign-bit matrix)\n",
      "   s_jj\n",
      "0     1\n",
      "1     1\n",
      "*** P (participation matrix, normalized.)\n",
      "      0.26\n",
      "      0.84\n",
      "\n",
      "*** Chi matrix O1 PT (MHz)\n",
      "    Diag is anharmonicity, off diag is full cross-Kerr.\n",
      "      28.6      189\n",
      "       189      313\n",
      "\n",
      "*** Chi matrix ND (MHz) \n",
      "       526     -492\n",
      "      -492      624\n",
      "\n",
      "*** Frequencies O1 PT (MHz)\n",
      "0    7209.141624\n",
      "1    7215.058834\n",
      "dtype: float64\n",
      "\n",
      "*** Frequencies ND (MHz)\n",
      "0    7432.783625\n",
      "1    6939.798837\n",
      "dtype: float64\n",
      "\n",
      "*** Q_coupling\n",
      "Empty DataFrame\n",
      "Columns: []\n",
      "Index: [0, 1]\n"
     ]
    },
    {
     "data": {
      "text/markdown": [
       "#### Mode frequencies (MHz)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "###### Numerical diagonalization"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Lj</th>\n",
       "      <th>10</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eigenmode</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7209.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7215.06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Lj              10\n",
       "eigenmode         \n",
       "0          7209.14\n",
       "1          7215.06"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "#### Kerr Non-linear coefficient table (MHz)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "###### Numerical diagonalization"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lj</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">10</th>\n",
       "      <th>0</th>\n",
       "      <td>526.43</td>\n",
       "      <td>-492.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-492.01</td>\n",
       "      <td>624.37</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "           0       1\n",
       "Lj                  \n",
       "10 0  526.43 -492.01\n",
       "   1 -492.01  624.37"
      ]
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     "metadata": {},
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    }
   ],
   "source": [
    "#Note: The method will connect to Ansys, activate_eigenmode_design(), add_eigenmode_setup().\n",
    "\n",
    "all_sweeps, return_code = eig_qres.run_sweep(readout.name,\n",
    "                                        'total_length', \n",
    "                                        ['10mm', '11mm', '12mm'],\n",
    "                                        render_qcomps,\n",
    "                                        open_terminations,\n",
    "                                         ignored_jjs=render_ignored_jjs,\n",
    "                                        design_name=\"GetEigenModeSolution\", \n",
    "                                       box_plus_buffer=box_plus_buffer\n",
    "                                      )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['10mm', '11mm', '12mm'])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_sweeps.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['option_name', 'variables', 'sim_variables'])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For example, just one group of solution data.\n",
    "all_sweeps['10mm'].keys()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'option_name': 'total_length',\n",
       " 'variables': {'energy_elec': 5.19788159957566e-25,\n",
       "  'energy_elec_sub': 4.2287113157029e-25,\n",
       "  'energy_mag': 8.01738203686217e-27},\n",
       " 'sim_variables': {'sim_setup_name': 'sweeper_em_setup',\n",
       "  'convergence_t':              Solved Elements  Max Delta Freq. %\n",
       "  Pass Number                                    \n",
       "  1                      12372                NaN\n",
       "  2                      16089           46.90700\n",
       "  3                      20923           26.83400\n",
       "  4                      27201           12.39700\n",
       "  5                      35009            5.13720\n",
       "  6                      45251            3.05090\n",
       "  7                      58833            1.58050\n",
       "  8                      76494            1.37040\n",
       "  9                      99448            0.75837\n",
       "  10                    129285            0.63878\n",
       "  11                    168080            0.27581\n",
       "  12                    218509            0.35048\n",
       "  13                    284068            0.39353\n",
       "  14                    369273            0.37846\n",
       "  15                    480014            0.23127,\n",
       "  'convergence_f':          re(Mode(1)) [g]  re(Mode(2)) [g]\n",
       "  Pass []                                  \n",
       "  1               5.569390        11.880396\n",
       "  2               4.638777         6.307600\n",
       "  3               5.883535         7.416275\n",
       "  4               6.612905         7.967902\n",
       "  5               6.952620         8.203356\n",
       "  6               7.139302         8.453635\n",
       "  7               7.252139         8.571936\n",
       "  8               7.325233         8.689408\n",
       "  9               7.365970         8.755306\n",
       "  10              7.413022         8.793678\n",
       "  11              7.433468         8.810855\n",
       "  12              7.459521         8.825424\n",
       "  13              7.488876         8.834471\n",
       "  14              7.517219         8.844294\n",
       "  15              7.534604         8.856040}}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_sweeps['10mm']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'energy_elec': 5.19788159957566e-25,\n",
       " 'energy_elec_sub': 4.2287113157029e-25,\n",
       " 'energy_mag': 8.01738203686217e-27}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_sweeps['10mm']['variables']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Solved Elements</th>\n",
       "      <th>Max Delta Freq. %</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pass Number</th>\n",
       "      <th></th>\n",
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       "      <th>1</th>\n",
       "      <td>12372</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16089</td>\n",
       "      <td>46.90700</td>\n",
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       "      <th>3</th>\n",
       "      <td>20923</td>\n",
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       "      <th>4</th>\n",
       "      <td>27201</td>\n",
       "      <td>12.39700</td>\n",
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       "      <th>5</th>\n",
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       "      <th>6</th>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>58833</td>\n",
       "      <td>1.58050</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>76494</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>99448</td>\n",
       "      <td>0.75837</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>129285</td>\n",
       "      <td>0.63878</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>168080</td>\n",
       "      <td>0.27581</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>218509</td>\n",
       "      <td>0.35048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>284068</td>\n",
       "      <td>0.39353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>369273</td>\n",
       "      <td>0.37846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>480014</td>\n",
       "      <td>0.23127</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "             Solved Elements  Max Delta Freq. %\n",
       "Pass Number                                    \n",
       "1                      12372                NaN\n",
       "2                      16089           46.90700\n",
       "3                      20923           26.83400\n",
       "4                      27201           12.39700\n",
       "5                      35009            5.13720\n",
       "6                      45251            3.05090\n",
       "7                      58833            1.58050\n",
       "8                      76494            1.37040\n",
       "9                      99448            0.75837\n",
       "10                    129285            0.63878\n",
       "11                    168080            0.27581\n",
       "12                    218509            0.35048\n",
       "13                    284068            0.39353\n",
       "14                    369273            0.37846\n",
       "15                    480014            0.23127"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_sweeps['10mm']['sim_variables']['convergence_t']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
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       "      <th>4</th>\n",
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       "      <th>5</th>\n",
       "      <td>6.952620</td>\n",
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       "      <th>6</th>\n",
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       "      <th>7</th>\n",
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       "      <td>8.571936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>7.325233</td>\n",
       "      <td>8.689408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>7.365970</td>\n",
       "      <td>8.755306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>7.413022</td>\n",
       "      <td>8.793678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>7.433468</td>\n",
       "      <td>8.810855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>7.459521</td>\n",
       "      <td>8.825424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>7.488876</td>\n",
       "      <td>8.834471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>7.517219</td>\n",
       "      <td>8.844294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>7.534604</td>\n",
       "      <td>8.856040</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         re(Mode(1)) [g]  re(Mode(2)) [g]\n",
       "Pass []                                  \n",
       "1               5.569390        11.880396\n",
       "2               4.638777         6.307600\n",
       "3               5.883535         7.416275\n",
       "4               6.612905         7.967902\n",
       "5               6.952620         8.203356\n",
       "6               7.139302         8.453635\n",
       "7               7.252139         8.571936\n",
       "8               7.325233         8.689408\n",
       "9               7.365970         8.755306\n",
       "10              7.413022         8.793678\n",
       "11              7.433468         8.810855\n",
       "12              7.459521         8.825424\n",
       "13              7.488876         8.834471\n",
       "14              7.517219         8.844294\n",
       "15              7.534604         8.856040"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_sweeps['10mm']['sim_variables']['convergence_f']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Uncomment the next close simulation software. \n",
    "#eig_qres.sim.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Uncomment next line if you would like to close the gui\n",
    "#gui.main_window.close()"
   ]
  }
 ],
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  "celltoolbar": "Tags",
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "mimetype": "text/x-python",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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